Overview

Dataset statistics

Number of variables18
Number of observations65376
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 MiB
Average record size in memory201.2 B

Variable types

Numeric17
Categorical1

Alerts

LossesSeverity is highly imbalanced (57.3%)Imbalance
Torque has 5489 (8.4%) zerosZeros

Reproduction

Analysis started2024-01-15 12:47:34.132204
Analysis finished2024-01-15 12:47:52.191728
Duration18.06 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

HoleSection
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.605933
Minimum4.125
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:52.222029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4.125
5-th percentile6.125
Q18.5
median8.5
Q317.5
95-th percentile17.5
Maximum26
Range21.875
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.510259
Coefficient of variation (CV)0.38861665
Kurtosis-0.7977806
Mean11.605933
Median Absolute Deviation (MAD)2.375
Skewness0.51781811
Sum758749.5
Variance20.342436
MonotonicityNot monotonic
2024-01-15T20:47:52.277719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8.5 23592
36.1%
17.5 19218
29.4%
12.25 12043
18.4%
6.125 9392
 
14.4%
4.125 574
 
0.9%
26 557
 
0.9%
ValueCountFrequency (%)
4.125 574
 
0.9%
6.125 9392
 
14.4%
8.5 23592
36.1%
12.25 12043
18.4%
17.5 19218
29.4%
26 557
 
0.9%
ValueCountFrequency (%)
26 557
 
0.9%
17.5 19218
29.4%
12.25 12043
18.4%
8.5 23592
36.1%
6.125 9392
 
14.4%
4.125 574
 
0.9%

M_Depth
Real number (ℝ)

Distinct4272
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2046.3425
Minimum14
Maximum4285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:52.340141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile340
Q11172
median2098
Q32905
95-th percentile3654
Maximum4285
Range4271
Interquartile range (IQR)1733

Descriptive statistics

Standard deviation1048.3867
Coefficient of variation (CV)0.51232222
Kurtosis-1.0632441
Mean2046.3425
Median Absolute Deviation (MAD)865
Skewness-0.062448689
Sum1.3378169 × 108
Variance1099114.8
MonotonicityNot monotonic
2024-01-15T20:47:52.414533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2644 21
 
< 0.1%
2716 21
 
< 0.1%
2690 21
 
< 0.1%
2689 21
 
< 0.1%
2688 21
 
< 0.1%
2687 21
 
< 0.1%
2686 21
 
< 0.1%
2685 21
 
< 0.1%
2684 21
 
< 0.1%
2683 21
 
< 0.1%
Other values (4262) 65166
99.7%
ValueCountFrequency (%)
14 2
< 0.1%
15 3
< 0.1%
16 3
< 0.1%
17 3
< 0.1%
18 3
< 0.1%
19 3
< 0.1%
20 3
< 0.1%
21 3
< 0.1%
22 3
< 0.1%
23 3
< 0.1%
ValueCountFrequency (%)
4285 1
< 0.1%
4284 1
< 0.1%
4283 1
< 0.1%
4282 1
< 0.1%
4281 1
< 0.1%
4280 1
< 0.1%
4279 1
< 0.1%
4278 1
< 0.1%
4277 1
< 0.1%
4276 1
< 0.1%

RateofPenetration
Real number (ℝ)

Distinct11010
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.629834
Minimum0.06
Maximum616.014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:52.491551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile1.596
Q13.55
median6.432
Q312.318
95-th percentile33.333333
Maximum616.014
Range615.954
Interquartile range (IQR)8.768

Descriptive statistics

Standard deviation13.726629
Coefficient of variation (CV)1.2913305
Kurtosis141.55332
Mean10.629834
Median Absolute Deviation (MAD)3.558
Skewness6.9812363
Sum694936.01
Variance188.42034
MonotonicityNot monotonic
2024-01-15T20:47:52.567025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.818181818 107
 
0.2%
6.25 103
 
0.2%
6.896551724 102
 
0.2%
6 101
 
0.2%
7.5 101
 
0.2%
6.976744186 98
 
0.1%
7.407407407 95
 
0.1%
12.5 94
 
0.1%
8 90
 
0.1%
9.230769231 89
 
0.1%
Other values (11000) 64396
98.5%
ValueCountFrequency (%)
0.06 1
< 0.1%
0.1 1
< 0.1%
0.13 1
< 0.1%
0.14 1
< 0.1%
0.16 1
< 0.1%
0.18 1
< 0.1%
0.22 1
< 0.1%
0.2200220022 1
< 0.1%
0.23 1
< 0.1%
0.259965338 1
< 0.1%
ValueCountFrequency (%)
616.014 1
< 0.1%
517.758 1
< 0.1%
447.366 1
< 0.1%
316.09 1
< 0.1%
280.18 1
< 0.1%
279.95 1
< 0.1%
266.37 1
< 0.1%
247.59 1
< 0.1%
244.06 1
< 0.1%
224.13 1
< 0.1%

WeightonBit
Real number (ℝ)

Distinct22172
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.726198
Minimum0
Maximum79.4
Zeros65
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:52.832785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.676286
Q17.4668
median12.7
Q321.2
95-th percentile38.9
Maximum79.4
Range79.4
Interquartile range (IQR)13.7332

Descriptive statistics

Standard deviation11.09901
Coefficient of variation (CV)0.70576564
Kurtosis0.49852117
Mean15.726198
Median Absolute Deviation (MAD)6.293276
Skewness1.0450353
Sum1028115.9
Variance123.18803
MonotonicityNot monotonic
2024-01-15T20:47:52.912175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2 179
 
0.3%
9 173
 
0.3%
4.6 172
 
0.3%
9.9 172
 
0.3%
7.1 167
 
0.3%
9.6 165
 
0.3%
9.1 164
 
0.3%
7.3 164
 
0.3%
10.9 163
 
0.2%
9.7 162
 
0.2%
Other values (22162) 63695
97.4%
ValueCountFrequency (%)
0 65
0.1%
0.0044 1
 
< 0.1%
0.011 1
 
< 0.1%
0.0132 1
 
< 0.1%
0.015736 1
 
< 0.1%
0.0198 1
 
< 0.1%
0.020232 1
 
< 0.1%
0.088 1
 
< 0.1%
0.0946 1
 
< 0.1%
0.0968 1
 
< 0.1%
ValueCountFrequency (%)
79.4 1
< 0.1%
69.5442 1
< 0.1%
67.9206 1
< 0.1%
66.5 1
< 0.1%
66.4 1
< 0.1%
65.461 1
< 0.1%
64.6 1
< 0.1%
62.8342 1
< 0.1%
62.3656 1
< 0.1%
61.6792 1
< 0.1%

Rotation
Real number (ℝ)

Distinct13533
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.6048
Minimum0
Maximum457.27
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:52.987616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile85.4175
Q1130
median155.23
Q3186
95-th percentile246.01
Maximum457.27
Range457.27
Interquartile range (IQR)56

Descriptive statistics

Standard deviation48.620964
Coefficient of variation (CV)0.30273669
Kurtosis0.11779707
Mean160.6048
Median Absolute Deviation (MAD)27.23
Skewness0.38836536
Sum10499699
Variance2363.9982
MonotonicityNot monotonic
2024-01-15T20:47:53.064015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 687
 
1.1%
150 685
 
1.0%
120 559
 
0.9%
140 497
 
0.8%
180 418
 
0.6%
151 412
 
0.6%
178 409
 
0.6%
149 401
 
0.6%
148 397
 
0.6%
110 364
 
0.6%
Other values (13523) 60547
92.6%
ValueCountFrequency (%)
0 2
< 0.1%
1 2
< 0.1%
4 1
< 0.1%
4.65 1
< 0.1%
7 1
< 0.1%
9 2
< 0.1%
15 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 2
< 0.1%
ValueCountFrequency (%)
457.27 1
< 0.1%
407.95 1
< 0.1%
390 1
< 0.1%
351.55 1
< 0.1%
342.94 1
< 0.1%
341.62 1
< 0.1%
341.44 1
< 0.1%
340.42 1
< 0.1%
339.55 1
< 0.1%
338.26 1
< 0.1%

Torque
Real number (ℝ)

ZEROS 

Distinct6203
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2841294
Minimum0
Maximum12.3
Zeros5489
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:53.139331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.37
median3.3152
Q34.278
95-th percentile6
Maximum12.3
Range12.3
Interquartile range (IQR)1.908

Descriptive statistics

Standard deviation1.7219045
Coefficient of variation (CV)0.52431081
Kurtosis0.6289665
Mean3.2841294
Median Absolute Deviation (MAD)0.9548
Skewness0.10725872
Sum214703.24
Variance2.9649553
MonotonicityNot monotonic
2024-01-15T20:47:53.214577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5489
 
8.4%
3.1 649
 
1.0%
3.79 589
 
0.9%
3.2 562
 
0.9%
3.3 493
 
0.8%
3.8 473
 
0.7%
3.9 463
 
0.7%
3.5 456
 
0.7%
3 430
 
0.7%
3.4 427
 
0.7%
Other values (6193) 55345
84.7%
ValueCountFrequency (%)
0 5489
8.4%
0.001 11
 
< 0.1%
0.002 9
 
< 0.1%
0.003 5
 
< 0.1%
0.004 2
 
< 0.1%
0.005 2
 
< 0.1%
0.006 5
 
< 0.1%
0.007 2
 
< 0.1%
0.00737 31
 
< 0.1%
0.0077 5
 
< 0.1%
ValueCountFrequency (%)
12.3 1
< 0.1%
12.2 1
< 0.1%
11.4 1
< 0.1%
11.1 2
< 0.1%
11 1
< 0.1%
10.9 2
< 0.1%
10.8 1
< 0.1%
10.5 1
< 0.1%
10.4 1
< 0.1%
10.3 2
< 0.1%

StandpipePressure
Real number (ℝ)

Distinct6341
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2047.0252
Minimum9
Maximum3915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:53.299338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile952.65
Q11661
median2137.3
Q32541.85
95-th percentile2823.15
Maximum3915
Range3906
Interquartile range (IQR)880.85

Descriptive statistics

Standard deviation594.3688
Coefficient of variation (CV)0.29035734
Kurtosis-0.10346418
Mean2047.0252
Median Absolute Deviation (MAD)433.55
Skewness-0.6718113
Sum1.3382632 × 108
Variance353274.27
MonotonicityNot monotonic
2024-01-15T20:47:53.377790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2099 67
 
0.1%
2668 65
 
0.1%
2784 61
 
0.1%
2523 61
 
0.1%
2117 59
 
0.1%
2610 59
 
0.1%
2233 58
 
0.1%
2639 57
 
0.1%
2175 56
 
0.1%
2560 55
 
0.1%
Other values (6331) 64778
99.1%
ValueCountFrequency (%)
9 2
< 0.1%
13 1
 
< 0.1%
17 1
 
< 0.1%
20 1
 
< 0.1%
21.75 1
 
< 0.1%
23 4
< 0.1%
24 2
< 0.1%
25 2
< 0.1%
26 1
 
< 0.1%
26.1 2
< 0.1%
ValueCountFrequency (%)
3915 1
< 0.1%
3659 1
< 0.1%
3541 1
< 0.1%
3384 1
< 0.1%
3252 1
< 0.1%
3188 1
< 0.1%
3178 1
< 0.1%
3173 1
< 0.1%
3131 2
< 0.1%
3114 1
< 0.1%

FlowIn
Real number (ℝ)

Distinct3385
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.62076
Minimum0
Maximum1934
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:53.456435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile211
Q1478.1477
median593
Q3781.9432
95-th percentile920
Maximum1934
Range1934
Interquartile range (IQR)303.7955

Descriptive statistics

Standard deviation222.02979
Coefficient of variation (CV)0.37465746
Kurtosis-0.70734366
Mean592.62076
Median Absolute Deviation (MAD)139
Skewness-0.23806669
Sum38743175
Variance49297.228
MonotonicityNot monotonic
2024-01-15T20:47:53.538116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
493.9979 451
 
0.7%
491.3562 442
 
0.7%
496.6396 408
 
0.6%
488.7145 374
 
0.6%
235.1113 342
 
0.5%
224.5445 332
 
0.5%
237.753 328
 
0.5%
227.1862 324
 
0.5%
499.2813 315
 
0.5%
232.4696 309
 
0.5%
Other values (3375) 61751
94.5%
ValueCountFrequency (%)
0 1
< 0.1%
7.9251 1
< 0.1%
10 1
< 0.1%
37 1
< 0.1%
42.2672 1
< 0.1%
60 1
< 0.1%
65 1
< 0.1%
71.3259 1
< 0.1%
73.9676 1
< 0.1%
76.6093 1
< 0.1%
ValueCountFrequency (%)
1934 1
 
< 0.1%
1278 1
 
< 0.1%
1263 1
 
< 0.1%
1059.3217 1
 
< 0.1%
1056 1
 
< 0.1%
1017 3
 
< 0.1%
1016 4
 
< 0.1%
1015 11
< 0.1%
1014 11
< 0.1%
1013 11
< 0.1%

FlowOut
Real number (ℝ)

Distinct7147
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.890947
Minimum0
Maximum100.08
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:53.616628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.61
Q149.51
median57.695
Q371.5
95-th percentile85.9
Maximum100.08
Range100.08
Interquartile range (IQR)21.99

Descriptive statistics

Standard deviation15.557307
Coefficient of variation (CV)0.25976058
Kurtosis-0.50067747
Mean59.890947
Median Absolute Deviation (MAD)10.445
Skewness0.10999988
Sum3915430.5
Variance242.0298
MonotonicityNot monotonic
2024-01-15T20:47:53.691532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 657
 
1.0%
38.5 69
 
0.1%
38.49 66
 
0.1%
50.49 39
 
0.1%
55.26 37
 
0.1%
52.97 37
 
0.1%
51 37
 
0.1%
55.7 37
 
0.1%
55.34 36
 
0.1%
55.95 36
 
0.1%
Other values (7137) 64325
98.4%
ValueCountFrequency (%)
0 4
< 0.1%
4.97 1
 
< 0.1%
6 1
 
< 0.1%
6.29 1
 
< 0.1%
9.58 1
 
< 0.1%
10.38 1
 
< 0.1%
11.14 1
 
< 0.1%
11.51 1
 
< 0.1%
12.22 1
 
< 0.1%
12.37 1
 
< 0.1%
ValueCountFrequency (%)
100.08 1
< 0.1%
99.81 1
< 0.1%
99.76 1
< 0.1%
99.66 1
< 0.1%
99.55 1
< 0.1%
99.52 1
< 0.1%
99.51 1
< 0.1%
99.49 2
< 0.1%
99.47 1
< 0.1%
99.45 1
< 0.1%

PumpStroke
Real number (ℝ)

Distinct6824
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.122
Minimum6
Maximum242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:53.768082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile45
Q194.83
median115
Q3141
95-th percentile164.2
Maximum242
Range236
Interquartile range (IQR)46.17

Descriptive statistics

Standard deviation36.983286
Coefficient of variation (CV)0.32984862
Kurtosis-0.46841798
Mean112.122
Median Absolute Deviation (MAD)23
Skewness-0.4883368
Sum7330087.7
Variance1367.7634
MonotonicityNot monotonic
2024-01-15T20:47:53.847422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 717
 
1.1%
96 684
 
1.0%
104 566
 
0.9%
95 550
 
0.8%
114 542
 
0.8%
97 533
 
0.8%
49 530
 
0.8%
120 499
 
0.8%
98 495
 
0.8%
115 481
 
0.7%
Other values (6814) 59779
91.4%
ValueCountFrequency (%)
6 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
13.11 1
 
< 0.1%
14.57 1
 
< 0.1%
16 3
 
< 0.1%
16.03 1
 
< 0.1%
17 1
 
< 0.1%
17.23 8
< 0.1%
17.25 1
 
< 0.1%
ValueCountFrequency (%)
242 1
< 0.1%
237.12 1
< 0.1%
230.33 1
< 0.1%
222 1
< 0.1%
215.18 1
< 0.1%
212.8 1
< 0.1%
209.68 1
< 0.1%
198.82 1
< 0.1%
197.98 1
< 0.1%
193.07 1
< 0.1%

MudWeight
Real number (ℝ)

Distinct121
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.279383
Minimum66
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:53.914604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile70
Q175
median77
Q380
95-th percentile116
Maximum127
Range61
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.946598
Coefficient of variation (CV)0.13807623
Kurtosis8.3428887
Mean79.279383
Median Absolute Deviation (MAD)2
Skewness2.9531855
Sum5182969
Variance119.82802
MonotonicityNot monotonic
2024-01-15T20:47:53.994075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 10652
16.3%
75 10178
15.6%
77 7231
11.1%
78 5009
 
7.7%
80 3947
 
6.0%
79 3732
 
5.7%
82 3534
 
5.4%
81 3173
 
4.9%
72 2481
 
3.8%
74 2405
 
3.7%
Other values (111) 13034
19.9%
ValueCountFrequency (%)
66 19
 
< 0.1%
67 243
 
0.4%
68 1830
2.8%
69 622
 
1.0%
69.06383 1
 
< 0.1%
69.12766 1
 
< 0.1%
69.19149 1
 
< 0.1%
69.25532 1
 
< 0.1%
69.31915 1
 
< 0.1%
69.38298 1
 
< 0.1%
ValueCountFrequency (%)
127 5
 
< 0.1%
126 126
 
0.2%
125 106
 
0.2%
124 150
 
0.2%
123 339
0.5%
122 454
0.7%
121 348
0.5%
120 491
0.8%
119 201
0.3%
118 443
0.7%

FunnelViscosity
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.966196
Minimum29
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:54.066071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile35
Q139
median43
Q345
95-th percentile54
Maximum76
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.6564125
Coefficient of variation (CV)0.13164797
Kurtosis2.3112445
Mean42.966196
Median Absolute Deviation (MAD)3
Skewness0.95447328
Sum2808958
Variance31.995003
MonotonicityNot monotonic
2024-01-15T20:47:54.136769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
44 8625
13.2%
45 5490
 
8.4%
40 5001
 
7.6%
38 4880
 
7.5%
43 4852
 
7.4%
41 4439
 
6.8%
42 4215
 
6.4%
36 4113
 
6.3%
46 3445
 
5.3%
39 2469
 
3.8%
Other values (28) 17847
27.3%
ValueCountFrequency (%)
29 121
 
0.2%
30 436
 
0.7%
31 54
 
0.1%
32 91
 
0.1%
33 420
 
0.6%
34 577
 
0.9%
35 1773
 
2.7%
36 4113
6.3%
37 2399
3.7%
38 4880
7.5%
ValueCountFrequency (%)
76 72
 
0.1%
68 41
 
0.1%
65 141
 
0.2%
64 57
 
0.1%
62 44
 
0.1%
61 279
0.4%
60 327
0.5%
59 144
 
0.2%
58 128
 
0.2%
57 409
0.6%

PlasticViscosity
Real number (ℝ)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.033988
Minimum3
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:54.207234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median13
Q316
95-th percentile29
Maximum48
Range45
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.7253994
Coefficient of variation (CV)0.47922226
Kurtosis5.3698486
Mean14.033988
Median Absolute Deviation (MAD)3
Skewness1.9537205
Sum917486
Variance45.230998
MonotonicityNot monotonic
2024-01-15T20:47:54.279856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
11 6754
 
10.3%
13 6120
 
9.4%
16 6071
 
9.3%
15 5580
 
8.5%
10 5277
 
8.1%
12 4626
 
7.1%
8 3705
 
5.7%
14 3316
 
5.1%
7 2979
 
4.6%
6 2798
 
4.3%
Other values (32) 18150
27.8%
ValueCountFrequency (%)
3 193
 
0.3%
4 266
 
0.4%
5 1096
 
1.7%
6 2798
4.3%
7 2979
4.6%
8 3705
5.7%
9 2429
 
3.7%
10 5277
8.1%
11 6754
10.3%
12 4626
7.1%
ValueCountFrequency (%)
48 70
 
0.1%
46 208
0.3%
43 63
 
0.1%
42 244
0.4%
41 271
0.4%
40 55
 
0.1%
39 7
 
< 0.1%
37 408
0.6%
36 327
0.5%
35 345
0.5%

YieldPoint
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.093719
Minimum2
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:54.347435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q113
median15
Q319
95-th percentile25
Maximum47
Range45
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7336181
Coefficient of variation (CV)0.29412828
Kurtosis1.9332281
Mean16.093719
Median Absolute Deviation (MAD)3
Skewness0.58702048
Sum1052143
Variance22.40714
MonotonicityNot monotonic
2024-01-15T20:47:54.413236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
14 6647
10.2%
12 6416
 
9.8%
13 6148
 
9.4%
15 5814
 
8.9%
20 4881
 
7.5%
16 4469
 
6.8%
18 4176
 
6.4%
17 3853
 
5.9%
19 3635
 
5.6%
10 2935
 
4.5%
Other values (22) 16402
25.1%
ValueCountFrequency (%)
2 44
 
0.1%
3 276
 
0.4%
4 325
 
0.5%
5 300
 
0.5%
6 16
 
< 0.1%
8 763
 
1.2%
9 1106
 
1.7%
10 2935
4.5%
11 2658
4.1%
12 6416
9.8%
ValueCountFrequency (%)
47 72
 
0.1%
40 25
 
< 0.1%
32 1
 
< 0.1%
31 54
 
0.1%
30 126
 
0.2%
29 39
 
0.1%
28 256
 
0.4%
27 376
 
0.6%
26 895
1.4%
25 1651
2.5%

Gel_Strength10sec
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3574094
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:54.473839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile9
Maximum26
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2699189
Coefficient of variation (CV)0.52093312
Kurtosis14.929423
Mean4.3574094
Median Absolute Deviation (MAD)1
Skewness2.9523669
Sum284870
Variance5.1525319
MonotonicityNot monotonic
2024-01-15T20:47:54.536794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 23322
35.7%
4 18058
27.6%
5 9136
 
14.0%
2 3542
 
5.4%
6 2718
 
4.2%
7 2312
 
3.5%
8 1931
 
3.0%
9 1135
 
1.7%
12 1006
 
1.5%
10 841
 
1.3%
Other values (7) 1375
 
2.1%
ValueCountFrequency (%)
1 704
 
1.1%
2 3542
 
5.4%
3 23322
35.7%
4 18058
27.6%
5 9136
 
14.0%
6 2718
 
4.2%
7 2312
 
3.5%
8 1931
 
3.0%
9 1135
 
1.7%
10 841
 
1.3%
ValueCountFrequency (%)
26 64
 
0.1%
22 25
 
< 0.1%
18 189
 
0.3%
17 50
 
0.1%
13 164
 
0.3%
12 1006
1.5%
11 179
 
0.3%
10 841
1.3%
9 1135
1.7%
8 1931
3.0%

Gel_Strength10min
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8772944
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:54.591631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q37
95-th percentile10
Maximum27
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.739437
Coefficient of variation (CV)0.46610511
Kurtosis10.771777
Mean5.8772944
Median Absolute Deviation (MAD)1
Skewness2.6828513
Sum384234
Variance7.5045148
MonotonicityNot monotonic
2024-01-15T20:47:54.655809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 16354
25.0%
5 15771
24.1%
6 11680
17.9%
7 7048
10.8%
8 3996
 
6.1%
3 3050
 
4.7%
10 1837
 
2.8%
9 1447
 
2.2%
2 1106
 
1.7%
15 908
 
1.4%
Other values (11) 2179
 
3.3%
ValueCountFrequency (%)
1 125
 
0.2%
2 1106
 
1.7%
3 3050
 
4.7%
4 16354
25.0%
5 15771
24.1%
6 11680
17.9%
7 7048
10.8%
8 3996
 
6.1%
9 1447
 
2.2%
10 1837
 
2.8%
ValueCountFrequency (%)
27 64
 
0.1%
26 25
 
< 0.1%
20 181
 
0.3%
19 315
 
0.5%
18 143
 
0.2%
17 313
 
0.5%
15 908
1.4%
14 111
 
0.2%
13 55
 
0.1%
12 615
0.9%

Solid
Real number (ℝ)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.344224
Minimum4
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.9 KiB
2024-01-15T20:47:54.720520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q111
median13
Q316
95-th percentile31
Maximum65
Range61
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.0429939
Coefficient of variation (CV)0.42128412
Kurtosis9.9826153
Mean14.344224
Median Absolute Deviation (MAD)2
Skewness2.5105308
Sum937768
Variance36.517776
MonotonicityNot monotonic
2024-01-15T20:47:54.791615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
13 11743
18.0%
12 9424
14.4%
17 5873
9.0%
11 5612
8.6%
15 5353
8.2%
14 4803
7.3%
16 3319
 
5.1%
8 3040
 
4.7%
9 2675
 
4.1%
10 2272
 
3.5%
Other values (26) 11262
17.2%
ValueCountFrequency (%)
4 19
 
< 0.1%
5 181
 
0.3%
6 1199
 
1.8%
7 1628
 
2.5%
8 3040
 
4.7%
9 2675
 
4.1%
10 2272
 
3.5%
11 5612
8.6%
12 9424
14.4%
13 11743
18.0%
ValueCountFrequency (%)
65 88
 
0.1%
39 5
 
< 0.1%
38 137
 
0.2%
37 55
 
0.1%
36 342
0.5%
35 486
0.7%
34 605
0.9%
33 668
1.0%
32 747
1.1%
31 326
0.5%

LossesSeverity
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
No-Loss
49545 
Seepage Loss
12880 
Partial Loss
 
2647
Severe Loss
 
270
Complete Loss
 
34

Length

Max length13
Median length7
Mean length8.2071555
Min length7

Characters and Unicode

Total characters536551
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo-Loss
2nd rowNo-Loss
3rd rowNo-Loss
4th rowNo-Loss
5th rowNo-Loss

Common Values

ValueCountFrequency (%)
No-Loss 49545
75.8%
Seepage Loss 12880
 
19.7%
Partial Loss 2647
 
4.0%
Severe Loss 270
 
0.4%
Complete Loss 34
 
0.1%

Length

2024-01-15T20:47:54.863087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T20:47:54.921030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no-loss 49545
61.0%
loss 15831
 
19.5%
seepage 12880
 
15.9%
partial 2647
 
3.3%
severe 270
 
0.3%
complete 34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 130752
24.4%
o 114955
21.4%
L 65376
12.2%
N 49545
 
9.2%
- 49545
 
9.2%
e 39518
 
7.4%
a 18174
 
3.4%
15831
 
3.0%
S 13150
 
2.5%
p 12914
 
2.4%
Other values (9) 26791
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 340423
63.4%
Uppercase Letter 130752
 
24.4%
Dash Punctuation 49545
 
9.2%
Space Separator 15831
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 130752
38.4%
o 114955
33.8%
e 39518
 
11.6%
a 18174
 
5.3%
p 12914
 
3.8%
g 12880
 
3.8%
r 2917
 
0.9%
t 2681
 
0.8%
l 2681
 
0.8%
i 2647
 
0.8%
Other values (2) 304
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
L 65376
50.0%
N 49545
37.9%
S 13150
 
10.1%
P 2647
 
2.0%
C 34
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 49545
100.0%
Space Separator
ValueCountFrequency (%)
15831
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 471175
87.8%
Common 65376
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 130752
27.8%
o 114955
24.4%
L 65376
13.9%
N 49545
 
10.5%
e 39518
 
8.4%
a 18174
 
3.9%
S 13150
 
2.8%
p 12914
 
2.7%
g 12880
 
2.7%
r 2917
 
0.6%
Other values (7) 10994
 
2.3%
Common
ValueCountFrequency (%)
- 49545
75.8%
15831
 
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 536551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 130752
24.4%
o 114955
21.4%
L 65376
12.2%
N 49545
 
9.2%
- 49545
 
9.2%
e 39518
 
7.4%
a 18174
 
3.4%
15831
 
3.0%
S 13150
 
2.5%
p 12914
 
2.4%
Other values (9) 26791
 
5.0%

Interactions

2024-01-15T20:47:50.914474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:34.943792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:35.896823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:36.979379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:37.967636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:38.973370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:39.897154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:41.026408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:42.037687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:43.037725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:43.979542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:45.107870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.065843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.990416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:47.930672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:49.028657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:49.975741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:50.975974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:35.009037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:35.949794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:37.032116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:38.020611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:39.026454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:39.952897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:41.079447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:42.091173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:43.091379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:44.031366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:45.161908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.116366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:47.037876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:47.979711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:49.079560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:50.027647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:51.029724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:35.073254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:35.996903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:37.085346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:38.079212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:39.079428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:40.005667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:41.137649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:42.151972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:43.145618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:44.079465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:45.214404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.167798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:47.091380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:48.034519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:49.135581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-01-15T20:47:51.087699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-01-15T20:47:45.897263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.828593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:47.754204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:48.687434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:49.810870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:50.758096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:51.754379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:35.791140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:36.714514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:37.849709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:38.855742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:39.791044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:40.732296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:41.914148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:42.914169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:43.867677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:44.997315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:45.955485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.879617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:47.809996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:48.737939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:49.866534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:50.814475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:51.809074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:35.843868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:36.767420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:37.908803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:38.915661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:39.845834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:40.787153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:41.979493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:42.975447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:43.920592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:45.054112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.010795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:46.937871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:47.863460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:48.975777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:49.920917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-15T20:47:50.867870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-15T20:47:51.891457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-15T20:47:52.055689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

HoleSectionM_DepthRateofPenetrationWeightonBitRotationTorqueStandpipePressureFlowInFlowOutPumpStrokeMudWeightFunnelViscosityPlasticViscosityYieldPointGel_Strength10secGel_Strength10minSolidLossesSeverity
017.58234.08163330.8139.04.31084.0726.043.05127.072.036615788No-Loss
117.58244.54545535.2119.04.3877.0663.037.56116.072.036615788No-Loss
217.58255.17241432.9117.04.4780.0643.037.30113.072.036615788No-Loss
317.58268.33333325.2121.03.9950.0701.038.48123.072.036615788No-Loss
417.58277.59493732.0129.04.31214.0788.040.54138.072.036615788No-Loss
517.58289.37500040.7136.04.61397.0843.042.26148.072.036615788No-Loss
617.582912.00000036.8127.04.51394.0844.042.11148.072.036615788No-Loss
717.583020.68965537.0120.04.41398.0844.042.14148.072.036615788No-Loss
817.583112.24489835.5119.04.51397.0844.042.34148.072.036615788No-Loss
917.583224.00000029.5120.04.21395.0845.042.02148.072.036615788No-Loss
HoleSectionM_DepthRateofPenetrationWeightonBitRotationTorqueStandpipePressureFlowInFlowOutPumpStrokeMudWeightFunnelViscosityPlasticViscosityYieldPointGel_Strength10secGel_Strength10minSolidLossesSeverity
653668.539712.8087.1280202.353.13762570.85430.597172.0895.1381.04022174617Seepage Loss
653678.539722.4668.4392202.193.14502576.65430.597172.6792.6981.04022174617Seepage Loss
653688.539732.0768.6328201.673.22642605.65430.597173.0692.0681.04022174617Seepage Loss
653698.539745.8927.4778202.153.13762581.00430.597172.6293.6981.04022174617Seepage Loss
653708.539752.6227.6406202.083.08582585.35430.597173.5094.3481.04022174617Seepage Loss
653718.539763.1867.6846202.183.10062579.55430.597173.6493.6481.04022174617Seepage Loss
653728.539773.2648.1510202.043.14502585.35430.597172.3193.6981.04022174617Seepage Loss
653738.539783.89411.5368201.563.21902610.00430.597171.2993.5581.04022174617Seepage Loss
653748.539794.45811.0968201.313.21902618.70427.955471.2093.1681.04022174617Seepage Loss
653758.539806.4989.9352201.483.24862610.00430.597171.5092.2081.04022174617Seepage Loss